To address the problems of tiny objects and high resolution of object detection in remote sensing imagery, the methods with coarse-grained image cropping have been widely studied. However, these methods are always inefficient and complex due to the two-stage architecture and the huge computation for split images. For these reasons, this article employs YOLO and presents an improved architecture, NRT-YOLO. Specifically, the improvements can be summarized as: extra prediction head and related feature fusion layers; novel nested residual Transformer module, C3NRT; nested residual attention module, C3NRA; and multi-scale testing. The C3NRT module presented in this paper could boost accuracy and reduce complexity of the network at the same time. Moreover, the effectiveness of the proposed method is demonstrated by three kinds of experiments. NRT-YOLO achieves 56.9% mAP0.5 with only 38.1 M parameters in the DOTA dataset, exceeding YOLOv5l by 4.5%. Also, the results of different classifications show its excellent ability to detect small sample objects. As for the C3NRT module, the ablation study and comparison experiment verified that it has the largest contribution to accuracy increment (2.7% in mAP0.5) among the improvements. In conclusion, NRT-YOLO has excellent performance in accuracy improvement and parameter reduction, which is suitable for tiny remote sensing object detection.

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Abstract. This paper proposes a fast detection method for changed land using UAV remote sensing image. Firstly, a series of SURF feature points on the old time phase orthoimage are retrieved and stored into a matching database. Secondly, through the establishment of coarsefine double matching recursive model to quickly match the new and old time phase images, Whether the change of land use is preliminarily determined by the matching of feature points. Finally, it can be accurately determined whether the suspicious land is appeared through comparative analysis which based on the spectrum and texture of the dual mode comprehensive contrast detection method .Compared to the traditional technology, this method does not need to lay a large number of ground control points, and does not need to splice the UAV images, but can realize the rapid automatic discovery of suspicious objects, which is not only high precision but also saves time and effort.